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Has this thought ever crossed your mind about how ChatGPT, Gemini, DeepSeek, or Microsoft Copilot can understand and respond to you like a human? You may have questions and curiosity about how these tools work and the driving force that makes it possible to mimic human intelligence.
To satisfy your curiosity we will give you a hint: Natural Language Processing (NLP)! NLP is the phenomena that make it possible and give machines the power to understand and respond like a human. Let’s discover more about NLP with WeCloudData. This introductory blog to NLP will answer your major questions and can help you understand the world of Generative AI. To learn more about GenAI read this blog here.
Natural language processing (NLP) is a subfield of AI that uses machine learning techniques enabling machines\ computers to understand, interpret, manipulate, and generate human languages. NLP acts as a bridge between human communication and computer understanding, hence allowing computers to process and analyze huge amounts of textual data.
NLP leads the era of Generative AI. From the ability of image generation models to the assistance skills of
like ChatGPT, Natural Language Processing is part of our day-to-day activities. Other examples powered by NLP include chatbots for customer service with spoken commands, and digital assistants like Amazon’s Alexa, and Apple’s Siri.
To enable machines to understand interpret and generate the human language, natural processing uses these fundamental methods.
Tokenization is the process of breaking textual data into small chunks or tokens. These tokens can be words, phrases, symbols, or other meaningful elements. Here are some examples to understand how tokenization works.
Examples:
Input Text: “The quick brown fox jumps over the lazy dog.”
Tokens: [“The”, “quick”, “brown”, “fox”, “jumps”, “over”, “the”, “lazy”, “dog”, “.”]
Input text: “Hello, world! How are you?”
Tokens: [“Hello”, “,”, “world”, “!”, “How”, “are”, “you”, “?”]
Parsing is the process of analyzing the grammatical structure of a sentence to understand the relationships between words. It builds a parse tree demonstrating how words are grouped into phrases and how those phrases are related to each other.
Example:
Input Text: “The cat chased the mouse.”
Parse Tree :
Parse Tree Explanation
S (Sentence): This is the root of the tree, representing the entire sentence.
NP (Noun Phrase): These phrases act as subjects or objects in the sentence.
VP (Verb Phrase): This phrase contains the verb and any related objects or modifiers.
DT (Determiner): These are words like “the,” “a,” or “an,” which specify nouns.
NN (Noun): These are words that represent people, places, or things.
V (Verb): These are words that describe actions.
Lemmatization is the process of reducing words to their root form, allowing for the grouping of different forms of the same word.
Example:
Words: “running”, “runs”, “ran”
Lemma: “Run”
NER is one of the very important techniques used in NLP. It is used to identify entities such as persons, organizations, locations, and other named items in the text.
Example:
Input Text: “Apple Inc. is headquartered in Cupertino, California.”
NER Results:
NLP has many applications across multiple domains, here are some of the key areas where its application is more prominent and impactful.
With the boom in Generative AI, many businesses have integrated AI power Chatbots in their websites and applications which provide customer assistance without delay. These chatbots use Natural Language Processing (NLP) to understand the customer query in real-time, understand it, and give answers like a real human.
NLP is fundamental to the functioning of search engines, enabling them to understand user queries and provide relevant results.
Gmail uses NLP to filter out spam and categorize emails, helping users manage their inboxes more effectively.
Language Translation
Google Translate uses NLP techniques to provide real-time language translation, breaking down language barriers and enhancing communication.
NLP has automated appointment schedules and common patient inquiries in the healthcare industry. Now these queries can be answered by AI agents or bots integrated with the hospital systems or website.
Bank of America uses an AI-powered chatbot, Erica, to help customers with financial queries. Erica has responded to 800 million inquiries from over 42 million clients and provided personalized insights and guidance over 1.2 billion times.
If you’re interested in learning NLP, WeCloudData offers a specialized Natural Language Processing (NLP) course that covers foundational concepts, deep learning techniques, and hands-on projects with real-world datasets. This course is ideal for professionals aiming to build careers in AI and NLP.
Python is the backbone of modern Natural Language Processing due to its extensive libraries and frameworks. Whether it’s training large language models, performing text preprocessing, or deploying NLP applications, Python remains the go-to language for developers and researchers.
At WeCloudData, we understand the significance of Python in AI and data science, which is why we offer comprehensive Python courses alongside our NLP program. These courses are designed to help beginners and professionals build strong programming foundations for AI and machine learning applications.
WeCloudData not only offers short courses but also provides a comprehensive range of resources to support your learning journey. These include live public training sessions led by industry experts, career workshops to prepare you for the job market, dedicated career services, and portfolio support to help showcase your skills to potential employers.
Join our applied NLP course at Weclouddata. Our Natural Language Processing Course is online and you can start anytime and get certified in natural processing language to show your skills.
Join WeCloudData to kickstart your learning journey and unlock new career opportunities in this Artificial Intelligence.
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